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The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks

Tutkimustuotosvertaisarvioitu

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The gene regulatory network for breast cancer : Integrated regulatory landscape of cancer hallmarks. / Emmert-Streib, Frank; Simoes, Ricardo de Matos; Mullan, Paul; Haibe-Kains, Benjamin; Dehmer, Matthias.

julkaisussa: Frontiers in Genetics, Vuosikerta 5, Nro FEB, Article 15, 2014.

Tutkimustuotosvertaisarvioitu

Harvard

Emmert-Streib, F, Simoes, RDM, Mullan, P, Haibe-Kains, B & Dehmer, M 2014, 'The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks', Frontiers in Genetics, Vuosikerta. 5, Nro FEB, Article 15. https://doi.org/10.3389/fgene.2014.00015

APA

Emmert-Streib, F., Simoes, R. D. M., Mullan, P., Haibe-Kains, B., & Dehmer, M. (2014). The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks. Frontiers in Genetics, 5(FEB), [Article 15]. https://doi.org/10.3389/fgene.2014.00015

Vancouver

Author

Emmert-Streib, Frank ; Simoes, Ricardo de Matos ; Mullan, Paul ; Haibe-Kains, Benjamin ; Dehmer, Matthias. / The gene regulatory network for breast cancer : Integrated regulatory landscape of cancer hallmarks. Julkaisussa: Frontiers in Genetics. 2014 ; Vuosikerta 5, Nro FEB.

Bibtex - Lataa

@article{adf95f165d9049eaa1a783a9f5566dc5,
title = "The gene regulatory network for breast cancer: Integrated regulatory landscape of cancer hallmarks",
abstract = "In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.",
keywords = "BC3Net, Breast cancer, Computational genomics, Gene regulatory network, GPEA, Statistical inference",
author = "Frank Emmert-Streib and Simoes, {Ricardo de Matos} and Paul Mullan and Benjamin Haibe-Kains and Matthias Dehmer",
year = "2014",
doi = "10.3389/fgene.2014.00015",
language = "English",
volume = "5",
journal = "Frontiers in Genetics",
issn = "1664-8021",
publisher = "Frontiers Media",
number = "FEB",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - The gene regulatory network for breast cancer

T2 - Integrated regulatory landscape of cancer hallmarks

AU - Emmert-Streib, Frank

AU - Simoes, Ricardo de Matos

AU - Mullan, Paul

AU - Haibe-Kains, Benjamin

AU - Dehmer, Matthias

PY - 2014

Y1 - 2014

N2 - In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

AB - In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

KW - BC3Net

KW - Breast cancer

KW - Computational genomics

KW - Gene regulatory network

KW - GPEA

KW - Statistical inference

UR - http://www.scopus.com/inward/record.url?scp=84897675326&partnerID=8YFLogxK

U2 - 10.3389/fgene.2014.00015

DO - 10.3389/fgene.2014.00015

M3 - Article

VL - 5

JO - Frontiers in Genetics

JF - Frontiers in Genetics

SN - 1664-8021

IS - FEB

M1 - Article 15

ER -